antinucleon commented on issue #5427:
URL: https://github.com/apache/incubator-tvm/issues/5427#issuecomment-619423150
Before the parser is fixed, in case someone needs a parser in a hurry, here
is my emergency use parser:
```
import re
import random
import tvm
from tvm import auto_scheduler
from tvm import relay
class ReRelayParser(object):
def __init__(self, expr_text, param_dict):
self.memo = {}
self.expr_text = expr_text
self.param_dict = param_dict
def _extract_args(self):
ARG_LIST = re.compile(r'fn\s\((.*?)\)\s->')
ARGS =
re.compile(r'\%([a-zA-Z0-9_]+):\sTensor\[\(([0-9,\s]+)\),\s([a-z0-9]+)\]')
arg_list = ARG_LIST.findall(self.expr_text)[0]
args = ARGS.findall(arg_list)
ret = []
for name, ss, dtype in args:
cmd = "{name} = relay.var('{name}', shape=[{shape}],
dtype='{dtype}')".format(name=name, shape=ss, dtype=dtype)
ret.append(cmd)
self.memo["%" + name] = name
return ret
def _extract_body(self):
ret = []
STMT =
re.compile(r'(\%\d+)\s=\s([a-zA-Z0-9._]+)\(([a-zA-Z\%0-9,\s_=\[\]"]+)\)')
LAST_STMT =
re.compile(r'([a-zA-Z0-9._]+)\(([a-zA-Z\%0-9,\s_=\[\]"]+)\)')
def random_name(length=8):
name = ""
for i in range(length):
name += chr(random.randint(97, 122))
return name
def process_args(args):
VAR_ARGS = re.compile(r'(%[0-9a-zA-z_]+)')
var_arg = VAR_ARGS.findall(args)
var_len = len(var_arg)
tmp = args.split(",")
return var_arg, ",".join(tmp[var_len:])
tmp = STMT.findall(self.expr_text)
for ss in tmp:
output = ss[0]
op = ss[1]
args = ss[2]
self.memo[output] = random_name()
var_args, attrs = process_args(args)
cmd = "{name} = relay.{op}({var_args}, {attrs})".format(
name=self.memo[output],
op=op,
var_args=", ".join([self.memo[x] for x in var_args]),
attrs=attrs
)
ret.append(cmd)
op, args = LAST_STMT.findall(self.expr_text)[-1]
var_args, attrs = process_args(args)
cmd = "output_expr = relay.{op}({var_args}, {attrs})".format(
op=op,
var_args=", ".join([self.memo[x] for x in var_args]),
attrs=attrs
)
ret.append(cmd)
return ret
def _convert_parmas(self):
ret = {}
for key in self.param_dict.keys():
new_key = "v" + key
ret[new_key] = self.param_dict[key]
return ret
def convert(self):
cmds = []
cmds.extend(self._extract_args())
cmds.extend(self._extract_body())
new_params = self._convert_parmas()
l = {}
exec("\n".join(cmds), None, l)
output_expr = l["output_expr"]
func = relay.Function(relay.analysis.free_vars(output_expr),
output_expr)
# mod = tvm.ir.IRModule.from_expr(output_expr)
return func, new_params
if __name__ == "__main__":
from PIL import Image
from matplotlib import pyplot as plt
import numpy as np
from tvm.contrib.download import download_testdata
image_url =
'https://github.com/dmlc/mxnet.js/blob/master/data/cat.png?raw=true'
image_path = download_testdata(image_url, 'cat.png', module='data')
resized_image = Image.open(image_path).resize((224, 224))
#plt.imshow(resized_image)
#plt.show()
image_data = np.asarray(resized_image).astype("float32")
# Add a dimension to the image so that we have NHWC format layout
image_data = np.expand_dims(image_data, axis=0)
synset_url = ''.join(['https://gist.githubusercontent.com/zhreshold/',
'4d0b62f3d01426887599d4f7ede23ee5/raw/',
'596b27d23537e5a1b5751d2b0481ef172f58b539/',
'imagenet1000_clsid_to_human.txt'])
synset_name = 'imagenet1000_clsid_to_human.txt'
synset_path = download_testdata(synset_url, synset_name, module='data')
with open(synset_path) as f:
synset = eval(f.read())
# Preprocess image as described here:
#
https://github.com/tensorflow/models/blob/edb6ed22a801665946c63d650ab9a0b23d98e1b1/research/slim/preprocessing/inception_preprocessing.py#L243
#image_data[:, :, :, 0] = 2.0 / 255.0 * image_data[:, :, :, 0] - 1
#image_data[:, :, :, 1] = 2.0 / 255.0 * image_data[:, :, :, 1] - 1
#image_data[:, :, :, 2] = 2.0 / 255.0 * image_data[:, :, :, 2] - 1
print('input', image_data.shape)
input_tensor = "float_image_input"
input_shape = (1, 224, 224, 3)
input_dtype = "float32"
#############################################################
with open("tmp.txt") as fi:
netdef = fi.read()
with open("tmp.params", "rb") as fi:
net_params = relay.load_param_dict(fi.read())
mod, params = ReRelayParser(netdef, net_params).convert()
target = "llvm -mcpu=core-avx2"
with relay.build_config(opt_level=3):
c_graph, c_lib, c_params = relay.build(mod, target, params=params)
from tvm.contrib import graph_runtime as runtime
# Create a runtime executor module
module1 = runtime.create(c_graph, c_lib, tvm.cpu())
# Feed input data
module1.set_input(input_tensor, tvm.nd.array(image_data))
# Feed related params
module1.set_input(**c_params)
# Run
module1.run()
# Get output
tvm_output1 = module1.get_output(0).asnumpy()
top1 = np.argmax(tvm_output1)
print('Prediction top-1:', top1, synset[top1])
```
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